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The Economics of Multi-Hop Ride Sharing

Creating New Mobility Networks Through IS

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Abstract

Ride sharing allows to share costs of traveling by car, e.g., for fuel or highway tolls. Furthermore, it reduces congestion and emissions by making better use of vehicle capacities. Ride sharing is hence beneficial for drivers, riders, as well as society. While the concept has existed for decades, ubiquity of digital and mobile technology and user habituation to peer-to-peer services and electronic markets have resulted in particular growth in recent years. This paper explores the novel idea of multi-hop ride sharing and illustrates how information systems can leverage its potential. Based on empirical ride sharing data, we provide a quantitative analysis of the structure and the economics of electronic ride sharing markets. We explore the potential and competitiveness of multi-hop ride sharing and analyze its implications for platform operators. We find that multi-hop ride sharing proves competitive against other modes of transportation and has the potential to greatly increase ride availability and city connectedness, especially under high reliability requirements. To fully realize this potential, platform operators should implement multi-hop search, assume active control of pricing and booking processes, improve coordination of transfers, enhance data services, and try to expand their market share.

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Notes

  1. While writing this paper, Carpooling.com has been taken over by its France-based competitor BlaBlaCar which, however, does not reduce the validity of our data, analyses, or conclusions as the market models of both firms are virtually identical.

  2. These are (in descending order): Berlin (B), Hamburg (HH), Munich (M), Cologne (K), Frankfurt/Main (F), Stuttgart (S), Düsseldorf (D), Dortmund (DO), Essen (E), Bremen (HB), Leipzig (L), Dresden (DD), Hanover (H), Nuremberg (N), Duisburg (DU), Bochum (BO), Wuppertal (W), Bonn (BN), Bielefeld (BI), Mannheim (MA), and Karlsruhe (KA).

  3. In this study, we limit the analysis to rides with one transfer (i.e., two hops). Drews and Luxen (2013), found negligible improvements when allowing for more than two hops.

  4. This observation can be explained through the temporal trip distribution as shown in Fig. 2: the morning and evening ride supply peaks are fairly compact. Hence, given a short first-hop ride, waiting times above 80 minutes fail to tap into much extra supply, as the peak has flattened out by then.

  5. Based on the edges served by both direct and MHRS rides, MHRS trips are on average 10 % longer in distance, 17 % more expensive, and take 40 % more time than their direct counterparts.

  6. Anecdotal evidence confirms this notion, as Carpooling.com also retains fully booked rides in its search results, indicating that demand may surpass supply.

  7. The results do not change qualitatively for more complex, e.g., non-linear, relationships.

  8. This analysis is based on train prices discounted by 50 % to reflect the possibility of obtaining low-cost tickets or other discount programs (“BahnCard50”). The analysis favors MHRS even more when assuming regular train fares.

  9. This difference is significant for any conventional threshold. Using Fisher’s Exact Test with groups “direct only” vs. “direct + MHRS” and outcomes “link exists (#rides\(\ge \tau \)) and “no link (#rides \(<\tau \)),” and for all \(\tau \in \{1,2,\ldots ,20\},\) yields p-values \(<.0001\).

  10. Formally, this is given by \(\sqrt{p_1 p_2}\) where \(p_1\) and \(p_2\) denote the population figures (in millions) of city 1 and 2, respectively.

  11. Note that 90 % of all relations fall within a range from 80 to 600 km.

  12. The price decrease is 38.5 cents per 100  km for a 1 million increase in the geometric population mean. For the two largest cities this would suggest a reduction of 95 cents per 100 km compared with a reduction of 36.5 cents between the two smallest cities.

  13. These findings are in line with standard gravity models as used in transportation analysis (Erlander and Stewart 1990).

  14. http://techcrunch.com/2015/04/15/blablacar-acquires-its-biggest-competitor-carpooling-com-to-dominate-european-market/.

  15. Making it from one meeting point to another introduces hassle. Everyone who ever changed trains from Gare de L’Est to Gare de Lyon in Paris will certainly agree.

  16. These are conveniently located and should be interested in ride sharing activities for at least two reasons: i) Drivers are likely to fuel their cars and purchase other products and ii), petrol companies may improve their image by actively supporting ride sharing – a fuel-efficient and thus sustainable activity beyond all doubt.

  17. Such optimization could leverage the rich body of research on optimal transit design and train scheduling. See Cordeau et al. (1998) and Guihaire and Hao (2008) for comprehensive reviews.

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Acknowledgements

The authors would like to thank Matthias Hauser as well as seminar participants at the Karlsruhe Institute of Technology and the University of Würzburg.

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Authors and Affiliations

  1. Institute of Information Systems and Marketing (IISM), Karlsruhe Institute of Technology (KIT), Englerstraße 14, 76131, Karlsruhe, Germany

    Timm Teubner

  2. Department of Business Management, Universität Würzburg, 97070, Würzburg, Germany

    Christoph M. Flath

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  1. Timm Teubner
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Correspondence to Timm Teubner.

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Accepted after three revisions by the editors of the special issue.

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Teubner, T., Flath, C.M. The Economics of Multi-Hop Ride Sharing. Bus Inf Syst Eng 57, 311–324 (2015). https://doi.org/10.1007/s12599-015-0396-y

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